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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi INOVTEK Polbeng - Seri Informatika Jurnal Teknoinfo JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Data Science Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Journal of Engineering and Information Technology for Community Service Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Computer Science Jurnal Informatika: Jurnal Pengembangan IT Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science International Journal of Informatics and Data Science
Isnain, Auliya Rahman
Fakultas Teknik Dan Ilmu Komputer, Universitas Teknokrat Indonesia
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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Decision Support System for Platform Selection in E-Commerce Using the OWH-TOPSIS Method Wang, Junhai; Isnain, Auliya Rahman; Suryono, Ryan Randy; Rahmanto, Yuri; Mesran, Mesran; Setiawansyah, Setiawansyah
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5990

Abstract

Platforms in e-commerce are digital systems that allow online transactions to buy and sell products or services. E-commerce platforms also provide benefits for business actors because they are able to reach a wider market without geographical restrictions, while offering efficiency in business operations. The main problem in choosing a platform for e-commerce is often related to the sheer number of options available and the variety of criteria that must be considered. Criteria such as fees, platform popularity, transaction security, ease of use, features provided, as well as customer service support are important factors in determining the most suitable platform. The implementation of a decision support system to help select the optimal e-commerce platform by applying the OWH-TOPSIS method shows that this system can provide accurate and effective recommendations, so that it can be used as a reference for users in determining the e-commerce platform that suits their needs. The decision support system using the OWH-TOPSIS method provides an efficient and objective solution in the selection of e-commerce platforms. The results of the ranking of the best e-commerce platforms show that Platform D occupies the top position with the highest score value, which is 0.882. In second place is Platform E which obtained a score of 0.8599, followed by Platform A with a score of 0.8341.
Employee Performance Evaluation Using the Standard Method of Deviation Multi-Objective Optimization by Ratio Analysis Isnain, Auliya Rahman; Rahmanto, Yuri
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 2 No. 4 (2024): Volume 2 Number 4 October 2024
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v2i4.164

Abstract

Employee performance refers to the extent to which an employee can achieve the goals and expectations that have been set by the organization, both in terms of quantity and quality of work. Employee performance appraisals cover various aspects, such as productivity, skills, discipline, creativity, and the ability to adapt to change. The main problem in employee performance evaluation is often related to the accuracy of assessments of various aspects of performance. Employee performance appraisal is a systematic process to evaluate the extent to which an employee meets the standards set by the organization in carrying out his or her duties and responsibilities. The purpose of this study is to apply the SD-MOORA method in evaluating employee performance objectively and comprehensively in the evaluation process, improve the accuracy of assessment, and provide a clearer picture of employee performance based on relevant criteria. The results of employee performance evaluation using the SD-MOORA method show that Siti Aisyah and Dina Putri occupy the top position with the same preference value, which is 0.47715, which indicates that their performance is superior in meeting the evaluation criteria. Both of these employees demonstrated consistent performance across the various aspects measured. In second place, there is Ahmad Firdaus with a preference score of 0.42932, which also reflects a fairly good contribution, although slightly lower than the two employees in the first rank. These results provide guidance for management to identify the best performing employees as well as design appropriate development strategies for other employees to increase their contributions in the future.
ANALISIS SENTIMEN PENGGUNA MEDIA SOSIAL TERHADAP KEBIJAKAN KENAIKAN PAJAK HIBURAN MENGGUNAKAN METODE SVM (SUPPORT VECTOR MACHINE) Romadhona, Waldy; Isnain, Auliya Rahman
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5603

Abstract

Pemerintah Indonesia telah memberlakukan kenaikan pajak hiburan sebesar 40-75% atas aktivitas karaoke, diskotek, bar, dan mandi uap atau spa. melalui UU No 1 Tahun 2022 tentang Hubungan Keuangan Antara Pemerintah Pusat dan Pemerintahan Daerah (HKPD). Kebijakan ini menuai beragam sentimen dari masyarakat, baik pro maupun kontra. Berdasarkan permasalahan tersebut, penulis melakukan analisis ini untuk mengetahui sentimen masyarakat pada kebijakan kenaikan pajak hiburan dengan mengggunakan data yang didapatkan dari media sosial twitter. Metode yang dipakai adalah Support Vector Machine (SVM). Kemudian untuk mengukur kinerja klasifikasi SVM menggunakan metode RFE (Recursive Feature Elimination). Hasil penelitian menunjukkan bahwa pada metode SVM RFE (Recursive Feature Elimination) dengan nilai akurasi mencapai 95%, precision 99%, recall 94%, dan F1-Score 97%. Sedangkan hasil klasifikasi SVM tanpa menggunakan metode RFE dengan akurasi mencapai 93%, precission 85%, recall 94%, F1-Score 88%.
Multi-Criteria Decision Support System for Best Warehouse Performance Selection Using Combined Compromise Solution Method Wang, Junhai; Setiawansyah, Setiawansyah; Isnain, Auliya Rahman
Bulletin of Data Science Vol 4 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v4i2.7196

Abstract

Selecting the best performing warehouse is a strategic step in supporting the efficiency of the supply chain and distribution of goods. This research aims to design a multi-criterion-based decision support system in evaluating and determining the best warehouse using the Combined Compromise Solution (CoCoSo) method. This method was chosen for its ability to combine the strength of weighted average approaches and relative compromises between alternatives, resulting in more balanced and objective decisions. This research involves eight warehouse alternatives that are assessed based on a number of relevant performance criteria. The process starts from problem identification, determination of criteria, data collection, normalization, weighting, to the application of the CoCoSo method. The final results showed that Warehouse C obtained the highest score of 4.8155, followed by Warehouse E and Warehouse A, indicating that the three warehouses had the best performance. These findings are expected to be a reference in strategic decision-making related to warehousing management as well as the basis for the development of a data-based performance evaluation system.
Implementasi Algoritma Machine Learning untuk Klasifikasi Suara Lingkungan: Implementation of Machine Learning Algorithm for Environmental Sound Classification Wibowo, Ari; Isnain, Auliya Rahman
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1712

Abstract

Suara lingkungan memiliki peran signifikan dalam menentukan kualitas hidup manusia dan keseimbangan ekosistem. Dengan meningkatnya urbanisasi dan perubahan iklim, pemantauan suara lingkungan menjadi krusial. Penelitian ini mengimplementasikan algoritma Machine Learning untuk mengklasifikasikan suara lingkungan menggunakan dataset ESC-50. Fitur-fitur seperti Mel-Frequency Cepstral Coefficients (MFCCs) dan Chroma digunakan untuk ekstraksi ciri. Setelah pra-pemrosesan data, dilakukan pemodelan dengan berbagai algoritma, termasuk KNeighbors Classifier, Random Forest Classifier, dan Extra Tree Classifier, yang kemudian dipilih untuk tuning hyperparameter. Dengan parameter optimal, dilakukan training pada model terpilih dan diuji pada dataset uji. Hasil menunjukkan KNeighbors Classifier memiliki akurasi tertinggi sebesar 63%. Studi ini memberikan kontribusi pada pengembangan teknologi pemantauan suara lingkungan dan membuka prospek penerapan dalam manajemen kota yang lebih efisien. Studi lanjutan disarankan untuk eksplorasi fitur-fitur suara yang lebih spesifik, penggunaan teknik deep learning, dan penggunaan dataset yang lebih luas untuk solusi yang lebih canggih dalam pemahaman dan manajemen suara lingkungan secara global.
Decision Support System for Performance Assessment of the Best Salesperson with the Integration of Entropy and WASPAS Wang, Junhai; Setiawansyah; Isnain, Auliya Rahman
International Journal of Informatics and Data Science Vol. 2 No. 2 (2025): June 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i2.88

Abstract

The salesperson performance assessment is an important aspect of improving the effectiveness of a company's marketing strategy. However, this assessment process often faces the challenge of subjectivity, especially in determining the weights of the criteria used. To address this issue, this study implements a combination of the Entropy and WASPAS methods. The Entropy method is used to objectively determine the weights of the criteria based on data variation, while the WASPAS method is used to evaluate and rank alternatives. A case study was conducted on five salesperson personnel with the criteria used in selecting the best salesperson being sales target achievement, product mastery, communication skills, creativity, and work ethics. The results showed that Muhammad Iqbal (A3) ranked first with a score of 0.882, followed by Andi Saputra (A1) with a score of 0.796, Rizky Kurniawan (A5) with a score of 0.770, Budi Santoso (A2) with a score of 0.724, and Siti Rahmawati (A4) with a score of 0.655. The main contribution of this research is to present a more accurate and objective salesperson performance evaluation model through the integration of the Entropy–WASPAS method. This finding has practical implications for companies in selecting the best employees, identifying salesperson personnel with outstanding performance, and supporting strategic decision-making in human resource development in the marketing field.
Penerapan Teknik Ensemble Learning untuk Klasifikasi Jenis-jenis Anemia: Application of Ensemble Learning Technique for Classification of Anemia Types Priandika, Arjuna; Isnain, Auliya Rahman
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1721

Abstract

Anemia merupakan kondisi medis yang memerlukan diagnosis yang akurat untuk penanganan yang efektif. Penelitian ini mengeksplorasi penerapan teknik ensemble learning, khususnya stacking classifier, untuk klasifikasi jenis-jenis anemia. Teknik ini menggabungkan tiga model dasar: Random Forest, K-Nearest Neighbors (KNN), dan Gradient Boosting, dengan Logistic Regression sebagai estimator akhir. Data medis yang digunakan melibatkan berbagai fitur hematologi, dan preprocessing meliputi pembersihan, normalisasi, serta pembagian data. Evaluasi model dilakukan menggunakan cross-validation dengan 10 lipatan. Hasil penelitian menunjukkan bahwa stacking classifier mencapai akurasi keseluruhan 98%, dengan precision dan recall yang sangat baik di sebagian besar kelas. Kelas-kelas seperti Iron deficiency anemia, Leukemia, dan Other microcytic anemia menunjukkan precision 100%, sementara beberapa kelas dengan sampel kecil mengalami recall yang lebih rendah. Secara keseluruhan, model ini efektif dalam mengklasifikasikan jenis-jenis anemia dengan akurasi tinggi dan dapat diadaptasi untuk meningkatkan diagnosis medis lebih lanjut. Penelitian ini menyoroti potensi teknik ensemble dalam memperbaiki performa klasifikasi dan menyarankan eksplorasi lebih lanjut pada data dengan distribusi yang tidak merata
Pengembangan Aplikasi Kepegawaian Berbasis Web Menggunakan Framework CodeIgniter Menerapkan Model Waterfall Zeafli Pebriawan; Isnain, Auliya Rahman
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1841

Abstract

This research on developing web-based employee applications was carried out using a case study of BSIP Bandar Lampung with PHP programming, MYSQL and CodeIginter Framework. BSIP (Badan Standardisasi Instrumen Pertanian) is an organization responsible for the standards and quality of agricultural equipment in the area. The main focus of BSIP is to develop standards, conduct quality testing, and supervise the use of agricultural equipment in the sector in Bandar Lampung. In the process of developing web-based applications using the SDLC method, the waterfall model is a useful approach in designing information systems that emphasizes the simplification of stages in system design, making it suitable for use in developing information systems to monitor strategic plans. The development of this application has stages such as analysis, design, programming, and testing by checking the features available in the application. This website-based staffing application has features, namely, adding employee data, editing employee data, printing staffing data, and staffing reports in which there is a feature page for recording leave data for all employees. The data contained in the staffing application is stored in a database contained in the phpMyAdmin program which has been stored in a structured manner based on the data that has been stored in the staffing application. The results of the development are the use case display, activity diagram, and implementation of the program that has been made which displays the results of the login page, admin dashboard page, data input page, employee data, employee attendance report, print employee reports.
COMPARISON OF RANDOM FOREST, SUPPORT VECTOR MACHINE AND NAIVE BAYES ALGORITHMS TO ANALYZE SENTIMENT TOWARDS MENTAL HEALTH STIGMA Elisa, Putri; Isnain, Auliya Rahman
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1817

Abstract

Advances in technology, especially the internet, have significantly changed the way people communicate, including social media. Social media facilitates more effective and efficient online communication. Twitter has 18.45 million users in Indonesia by 2022. Discussion of mental health stigma on twitter, increased 17% in 2021 compared to the previous year. Lifestyle transformation, social pressures, and technological advancements have created new challenges in maintaining individual mental health. Discussions of mental health issues have become pros and cons on twitter. The tendency of twitter users in posting content can be known by means of sentiment analysis. Therefore, sentiment analysis can be used to classify comments and tweets related to mental health stigma into negative, positive and neutral. So, it is expected to provide a number of significant benefits in the aspect of managing mental health issues. The methods used to analyze sentiment towards mental health stigma are Random Forest, Support Vector Machine (SVM) and Naïve Bayes algorithms. Based on the research that has been done, it produces 3,095 data for the period 2020-2023. After preprocessing and labeling the data, 1,635 data (negative class), 633 data (positive class) and 208 data (neutral class) were obtained. The SVM model test results show an accuracy of 86.11%, the Random Forest model shows an accuracy of 82.55%, while the Naive Bayes model shows an accuracy of 78.19%. Therefore, it can be concluded that SVM has the best performance in classifying tweets containing mental health stigma.
SENTIMENT ANALYSIS OF PUBLIC OPINION ON THE RIGHT OF INQUIRY IN INDONESIA IN 2024 USING THE SUPPORT VECTOR MACHINE (SVM) METHOD Sebastian, Dicky Fernanda; Sulistiani, Heni; Isnain, Auliya Rahman
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1968

Abstract

Research on the right of inquiry refers to public responses on twitter social media related to the 2024 elections. The right of inquiry is a right used in investigations. There are a lot of public opinions about the right of inquiry that are discussed on twitter social media that convey their various opinions or criticisms of government policies towards the 2024 elections. Based on Law No. 17/2014, the right of inquiry of the House of Representatives is regulated in Article 20A of the 1945 Constitution, which regulates the right of inquiry of the House of Representatives. Sentiment analysis is used in this research to determine the accuracy value of public opinion which is categorized into two, namely positive and negative sentiment. In this study, the SVM method is used to identify and find the results of public opinions or responses regarding the issue of the right of inquiry in Indonesia in 2024 which is being widely under the twitter social media platform, so it is necessary to analyze the sentiment. By using the support vector machine (SVM) algorithm and word weighting using TF-IDF (term frequency-inverse document frequency). Data collection using Google Collaboratory tools with the python programming language. The data used were 2,179 tweets with the keywords "inquiry right", "DPR inquiry right", "election inquiry right". The results obtained from the SVM process with an accuracy value of 77%, negative precision value 77%, positive precision value 77%, negative recall value 57%, positive recall value 89%, positive f1-score value 66%, negative f1-score value 82%. The data that has been tested and processed has an adequate accuracy value for SVM algorithm classification using confusion matrix calculation. The results of the research conducted have been effective with the SVM method.
Co-Authors Ade Dwi Putra Ade Surahman Ady Chandra Agung Saputra Agus Sihabuddin Ahmad Ari Aldino Aidil Akbar Alita, Debby Andi Nurkholis Andrian, Eka Aprian Nuriansah Aprilia, Ni Wayan Ayu Sekar Ari Wibowo Bagus Miftaq Hurohman Bakti Eka Putra Deby Sintia Amelia Dedi Darwis Dinda Ayu Prasticha Donaya Pasha Elisa, Putri Elvano Delisa Mega Fatriade Saputra Fenty ariany Fikri Hamidy Heni Sulistiani Ikbal Yasin Ikbal Yasin Indah Sari Jepi Supriyanto Jepi Supriyanto Lestari, Rohmah Dewi Lili Andraini Linda Yunita M Agung Saputra Marga, Nurman Satya Mesran, Mesran Muhammad Indigo Muhammad Pajar Kharisma Muhammad Pajar Kharisma Putra Nadia Apriliza Neneng Neneng Nirwana Hendrastuty Nurman Satya Marga Nuroji Nuroji Oktavia, Ingrid Permata Permata Priandika, Adhie Thyo Priandika, Arjuna Priskilia Lovika Prita Dellia Qadhli Jafar Adrian Rido Febriansyah Rika Mersita Romadhona, Waldy Ryan Randy Suryono Sakti, Adam Indra Sanriomi Sintaro Saputra, Dany Eka Sebastian, Dicky Fernanda Setiawan, Samuel Budi Setiawansyah Setiawansyah Styawati Styawati Styawati, Styawati Taufiq Martiwansyah Very Hendra Saputra Wahyudi, Agung Deni Wang, Junhai Yasinta Ismi Yohanes Suyanto Yunita Yunita Yuri Rahmanto Zaenal Abidin Zai, Charles Zeafli Pebriawan Zulfa Nabila