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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 Jurnal Komputasi Journal of Decision Support Systems and Multi-Criteria Decision Making (JODESMA)
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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Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier Debby Alita; Auliya Rahman Isnain
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2615

Abstract

Kalimat sindiran atau sarkasme masih sering digunakan oleh kalangan publik untuk mengungkapkan maksud isi hati dan pikiran baik itu yang disampaikan secara langsng maupun tidak langsung. Sarkasme dilakukan untuk menyindir dan menyakiti hati seseorang dengan menggunakan bahasa atau kata yang didalamnya mengandung kata positif tetapi maknanya negatif sehingga sering sekali terjadi opini salah diklasifikasikan. Penelitian ini melakukan kombinasi antara proses sentimen analisis dengan deteksi sarkasme untuk pengklasifikasian opini yang terdapat pada Twitter. Proses analisis sentimen dilakukan dengan tahapan preprocessing dan ekstraksi fitur dan diklasifikan dengan menggunakan metode Support Vector Machine dilanjutkan dengan proses pendeteksian sarkasme yang dilakukan tahapan ekstraksi fitur dengan 4 set fitur yaitu sentiment related, punctuation-relate, lexical and syntactic, dan pattern-relate dan diklasifikasikan dengan menggunakan metode Random Forest Classifier. Hasil penelitian ini didapatkan peningkatan nilai rata-rata akurasi sebesar 16,61 %, nilai presisi sebesar 5,45 %, nilai recall sebesar 9,64% dan kenaikan nilai F1score sebesar 11,27% dengan jumlah data sebanyak 2.027 dengan rincian data dengan label positif berjumlah 1023, data dengan label negatif berjumlah 587 dan data dengan label netral berjumlah 462. Data sarkasme didapatkan dari tweet dengan label positif yang kemudian diberikan label sarkasme atau tidak sarkasme dan didapat hasil label dengan jumlah keseluruhan berlabel sarkasme berjumlah 354 dan tidak sarkasme berjumlah 669.
Penerapan Multiple Attribute Decision Making Menggunakan Weighting Product Untuk Pemilihan Laptop Auliya Rahman Isnain
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 1 (2026): Volume 4 Number 1 March 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i1.240

Abstract

In the process of choosing a laptop, problems often arise in the form of numerous alternatives with varied specifications, complex differences in user needs, limited accurate information, as well as difficulties in comparing various criteria such as price, performance, battery life, and device quality, resulting in decision-making that is less objective, inconsistent, and potentially leads to suboptimal choices. The purpose of this study for the selection of laptops used is based on price, RAM, processor, storage and size in recommending laptops using the WP method. The results of the system recommendation assessment using applications and manual calculations provide recommendations for Microsoft Surface Pro 6 Laptop ranked first with a final score based on the WP method getting a result of 0, 274854256. The test results get a value of 100% according to the system functionality testing using blackbox testing. Based on the results of data processing of respondents' responses as many as 16 respondents based on 4 TRITAM Model criteria, Trust results were obtained (Trust) of 77.92%, Risk of Use (Risk) of 75.83%, Perceived Usefulness of 89.79%, Perception of Ease of Use (Perceived easy of Use) of 81.04%. The overall test results using the TRITAM Model for technology acceptance were Good at 82.56%.
PENERAPAN NAÏVE BAYES CLASSIFIER UNTUK PENDUKUNG KEPUTUSAN PENERIMA BEASISWA Debby Alita; Indah Sari; Auliya Rahman Isnain; Styawati Styawati
Jurnal Data Mining dan Sistem Informasi Vol 2, No 1 (2021): Februari 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v2i1.1028

Abstract

Scholarships are the provision of assistance in the form of financial assistance provided to individuals with the aim of being used for the sustainability of the education achieved. The problem that occurs in this research is that the process of determining which is still carried out conventionally the student section must check one by one the scholarship application files submitted by students because each data will be compared one by one according to predetermined criteria, which results in the student section becoming difficult in the decision so that It takes a long time, therefore we need a decision support system that can help schools make decisions about scholarship recipients.The Naive Bayes Classifier method is a method that can be used in decision making to get better results on a classification problem. The purpose of this study is to build a scholarship recipient decision support system using the Naïve Bayes Classifier method. In this study, a problem analysis was carried out using PIECES analysis and for the system development method using.The result of this research is that applying the naïve Bayes method to the scholarship recipient's decision support system can assist the school in determining the scholarship recipient more quickly and accurately. The scholarship recipient's decision support system was built using the Java programming language and MySQL database. Keyword: Decision Support Systems, Naïve Bayes Classifier, Waterfall, Blackbox Testing, PIECES
SENTIMEN ANALISIS PUBLIK TERHADAP KEBIJAKAN LOCKDOWN PEMERINTAH JAKARTA MENGGUNAKAN ALGORITMA SVM Auliya Rahman Isnain; Adam Indra Sakti; Debby Alita; Nurman Satya Marga
Jurnal Data Mining dan Sistem Informasi Vol 2, No 1 (2021): Februari 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v2i1.1021

Abstract

Media sosial menjadikan masyarakat mengalami pergeseran perilaku baik budaya, etika dan norma yang ada, sehingga mereka dapat mengeluarkan opini-opini yang mereka miliki. Opini merupakan suatu pendapat dari pemikiran masayarakat mengenai suatu permasalahan yang sedang terjadi, saat ini Indonesia sedang dihadapkan oleh masalah mengenai virus Covid-19 yang memakan begitu banyak korban jiwa sehingga masyarakat mengeluarkan opini mereka mengenai virus tersebut dan kebijakan yang dilakukan pemerintah menghadapi virus tersebut.Penelitian ini bertujuan untuk mengetahui bagaimana sentiment publik terhadap kebijakan yang akan dilakukan pemerintah mengenai kebijakan lockdown ataupun pembatasan sosial berskala besar menggunakan metode Support Vector Machine denga ekstraksi fitur tf-idf  dengan pengujian yang nantinya akan dilihat bagaimana nilai accuracy, precision, Recall dan F1-Score.Penggunaan metode Support Vector Machine dan ekstraksi fitur dengan tf-idf yang membagi kelas menjadi sentiment positif 68,75% dan negative 31,25% menghasilkan nilai accuracy sebesar 74%, precision sebesar 75%, recall sebesar 92% dan F1-Score sebesar 83%.
PEMODELAN TOPIK DENGAN LDA UNTUK MEMAHAMI PERSEPSI PUBLIK TERHADAP APPLE VISION PRO MELALUI ANALISIS BERITA TWITTER Al farhan Irhanusa Ridat; Auliya Rahman Isnain
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6500

Abstract

Sebagai salah satu teknologi terbaru yang memperkenalkan pengalaman komputasi spasial melalui realitas virtual dan augmented reality, Apple Vision Pro mendapatkan perhatian luas dari publik. Oleh karena itu, penting untuk memahami bagaimana persepsi publik terhadap perangkat ini terbentuk dan berkembang di media sosial. Penelitian ini bertujuan untuk memahami persepsi publik tentang Apple Vision Pro melalui analisis topik menggunakan metode Latent Dirichlet Allocation (LDA). Data yang digunakan berupa 1.500 tweet terkait Apple Vision Pro yang dikumpulkan dari platform Twitter selama periode 1 Januari hingga 30 April 2024. Proses analisis dimulai dengan pengumpulan data, diikuti oleh tahap preprocessing yang mencakup pembersihan dan normalisasi teks. Setelah itu, corpus dan dictionary dibuat untuk merepresentasikan data dalam bentuk numerik. Model LDA kemudian digunakan untuk mengidentifikasi topik utama dari data yang telah diproses, dengan jumlah topik optimal ditentukan menggunakan Coherence Score. Hasil analisis menunjukkan enam topik utama yang mencakup diskusi mengenai harga, teknologi komputasi spasial, dan penerapan realitas virtual serta augmented reality. Nilai Coherence Score sebesar 0.4646 mengindikasikan bahwa model yang dihasilkan mampu mengelompokkan tema-tema utama dengan cukup koheren, memberikan wawasan mendalam tentang persepsi publik terhadap Apple Vision Pro di media sosial.
TEKNO Method: Total Evaluation based on Knowledge-driven Normalized Optimization for Multi-Criteria Decision Making Yuri Rahmanto; Ryan Randy Suryono; Dedi Darwis; Abhishek R. Mehta; Auliya Rahman Isnain
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.7

Abstract

Multi-criteria decision making (MCDM) methods play an important role in supporting decisions involving multiple criteria with different characteristics and levels of importance. However, differences in normalization procedures, criterion treatment, and aggregation mechanisms can lead to variations in the resulting preference structures. This study proposes a new MCDM method, namely total evaluation based on knowledge-driven normalized optimization (TEKNO), which integrates normalization, relative evaluation, criterion weighting, optimization-based normalization, and total evaluation into a unified decision-making framework. The proposed method is designed to transform heterogeneous decision information into comparable evaluation values while preserving the relative contribution of each criterion. The applicability of TEKNO is evaluated through two decision-making case studies involving new store location selection and leasing customer selection. The evaluation framework includes ranking analysis, comparison with established MCDM methods, Spearman rank correlation analysis, and sensitivity analysis under variations in criterion weights. The results show that TEKNO achieves a Spearman rank correlation coefficient of 1.0000 for the new store location case and 0.9964 for the leasing customer selection case, indicating very strong agreement with the reference rankings. In addition, the ranking remains unchanged across the tested sensitivity scenarios, demonstrating the stability of TEKNO under variations in criterion weights. These findings indicate that TEKNO provides a transparent, systematic, and stable alternative for MCDM applications for practical decision support where reliable ranking, methodological transparency, and robustness across alternative evaluation conditions are required. Nevertheless, broader validation using diverse datasets, decision domains, weighting schemes, and statistical evaluation techniques is required to further establish its generalizability and comparative performance.
Co-Authors Abhishek R. Mehta Adam Indra Sakti Ade Dwi Putra Ade Surahman Ady Chandra Agung Saputra Agus Sihabuddin Ahmad Ari Aldino Aidil Akbar Al farhan Irhanusa Ridat 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 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 Mesran, Mesran Muhammad Indigo Muhammad Pajar Kharisma Muhammad Pajar Kharisma Putra Nadia Apriliza Neneng Neneng Nirwana Hendrastuty Nurman Satya Marga Nurman Satya Marga Nuroji Nuroji Oktavia, Ingrid Pasha, Donaya Permata Permata Priandika, Adhie Thyo Priandika, Arjuna Priskilia Lovika Prita Dellia Qadhli Jafar Adrian Rido Febriansyah Rika Mersita Romadhona, Waldy Ryan Randy Suryono Sanriomi Sintaro Saputra, Dany Eka Sebastian, Dicky Fernanda Setiawan, Samuel Budi Setiawansyah Setiawansyah 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