Article Per Year (5 Year)
p-Index From 2021 - 2026
6.545
P-Index
This Author published in this journals
All Journal Teknika Journal of Economics, Business, & Accountancy Ventura SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Informatika dan Teknik Elektro Terapan JTT (Jurnal Teknologi Terpadu) Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Inotera Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika Jurnal Riset Informatika Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Ideguru: Jurnal Karya Ilmiah Guru Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) SINTA Journal (Science, Technology, and Agricultural) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer J-Intech (Journal of Information and Technology) Jurnal Indonesia Raya Knowbase : International Journal of Knowledge in Database Jurnal Dehasen Mengabdi SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Muhammad Fikry
Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 58 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 58 Documents
Search

 2   3   4   5   6 
Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pada Komentar Bitcoin Di Aplikasi X Yaskur Bearly Fernandes; Elin Haerani; Fadhilah Syafria; Muhammad Fikry; Lola Oktavia
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.928

Abstract

Social media has become a primary medium for users to express opinions, including those related to Bitcoin, whose fluctuating value often triggers diverse public responses. The large volume of unstructured comments makes manual sentiment analysis inefficient, thereby necessitating an automated approach based on machine learning. This study aims to classify positive and negative sentiments in Bitcoin-related comments on the X platform using the Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) feature weighting. The dataset consists of 1,750 Indonesian-language comments labeled by three annotators. The data were processed through several preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, and stemming. Model evaluation was conducted using four data split ratios, namely 90:10, 80:20, 70:30, and 60:40. The experimental results indicate that the 90:10 ratio achieved the best performance, with an accuracy of 72.57%, precision of 0.75, recall of 0.73, and an F1-score of 0.67. The SVM model demonstrates strong performance in identifying positive sentiments; however, it is less effective in detecting negative sentiments due to class imbalance in the dataset. As an additional experiment, testing was performed using a balanced dataset obtained through an undersampling process and several SVM kernel types for comparison. The results show that using a balanced dataset leads to more evenly distributed classification performance across sentiment classes, while the linear kernel provides the most stable performance compared to other kernels. Overall, SVM with TF-IDF weighting proves to be an effective approach for sentiment analysis of Bitcoin-related comments on social media.
Application of the K-Nearest Neighbor Method for Hypertension Disease Classification Diqti, Fadillah Fauziah; Khaidar, Al; Fikry, Muhammad; Asrianda, Asrianda
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8517

Abstract

Hipertensi merupakan salah satu penyakit tidak menular dengan prevalensi tinggi dan sering disebut sebagai silent killer karena sering tidak menunjukkan gejala. Penelitian ini bertujuan untuk mengklasifikasikan penyakit hipertensi menggunakan metode K-Nearest Neighbor (KNN). Data yang digunakan berjumlah 478 data pasien RSUD H. Sahudin dengan delapan atribut, yaitu usia, tekanan darah sistolik, tekanan darah diastolik, asam urat, kadar glukosa, kolesterol, berat badan, dan tinggi badan. Data dibagi menjadi 70% data latih dan 30% data uji. Hasil pengujian menunjukkan bahwa metode KNN dengan nilai K = 5 menghasilkan tingkat akurasi sebesar 81,25%. Hasil penelitian ini menunjukkan bahwa algoritma KNN efektif digunakan dalam proses klasifikasi penyakit hipertensi dan dapat membantu pengambilan keputusan di bidang kesehatan. 
Development of a Forward Chaining-Based Expert System for Web-Based Initial Screening of Mental Health Disorders Munadila, Aura; Asrianda, Asrianda; Fikry, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8521

Abstract

Kesehatan mental merupakan aspek penting dalam kesejahteraan individu yang memengaruhi kemampuan berpikir, mengelola stres, berinteraksi sosial, serta mengambil keputusan secara efektif. Peningkatan prevalensi gangguan kesehatan mental menuntut adanya solusi berbasis teknologi yang mampu membantu proses skrining dan diagnosis awal secara cepat dan mudah diakses. Penelitian ini bertujuan untuk mengembangkan sistem pakar berbasis web menggunakan metode Forward Chaining sebagai mekanisme inferensi dalam melakukan skrining awal gangguan kesehatan mental. Sistem yang dikembangkan mampu mengidentifikasi lima jenis gangguan kesehatan mental, yaitu gangguan kecemasan, serangan panik, Post Traumatic Stress Disorder (PTSD), skizofrenia, dan Obsessive Compulsive Disorder (OCD), berdasarkan 32 gejala yang diperoleh melalui studi literatur dan konsultasi dengan pakar psikologi. Pengujian sistem dilakukan menggunakan metode black-box testing, white-box testing, dan test with known cases. Hasil pengujian menunjukkan tingkat akurasi sebesar 82,14% pada pengujian black-box, serta akurasi 100% pada pengujian white-box dan test with known cases. Hasil penelitian menunjukkan bahwa metode Forward Chaining efektif diterapkan pada sistem pakar berbasis web sebagai alat bantu skrining awal gangguan kesehatan mental.
Development of E-TGA System Using SDLC Waterfall Method at Politeknik Negeri Lhokseumawe Rahmatillah, Siska Yuna; Asrianda, Asrianda; Fikry, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8516

Abstract

Pengelolaan Tugas Akhir di Politeknik Negeri Lhokseumawe masih menghadapi berbagai permasalahan, antara lain proses Administrasi yang belum terintegrasi, penggunaan dokumen fisik, keterbatasan monitoring, serta kesulitan dalam pencarian dan pengelolaan arsip Tugas Akhir. Kondisi tersebut berdampak pada rendahnya efisiensi, transparansi, dan kualitas layanan akademik. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem E-TGA (Elektronik Tugas Akhir) berbasis web sebagai solusi digital dalam pengelolaan Tugas Akhir mahasiswa secara terintegrasi. Metode penelitian yang digunakan adalah Research and Development (RD) dengan pendekatan mixed methods, serta model pengembangan perangkat lunak Software Development Life Cycle (SDLC) menggunakan metode Waterfall. Sistem E-TGA dirancang untuk memfasilitasi penyerahan dokumen Tugas Akhir secara daring, verifikasi dan validasi dokumen oleh petugas perpustakaan, monitoring data oleh dosen pembimbing, serta penyimpanan repository dokumen Tugas Akhir secara digital. Pengujian sistem dilakukan menggunakan metode black box testing untuk memastikan seluruh fungsi berjalan sesuai dengan kebutuhan pengguna. Hasil penelitian menunjukkan bahwa sistem E-TGA mampu meningkatkan efektivitas dan efisiensi proses pengelolaan Tugas Akhir, mengurangi ketergantungan pada dokumen fisik, mempercepat proses verifikasi, serta meningkatkan akurasi pencarian data melalui fitur pencarian berbasis query SQL dengan operator LIKE dan OR. Dengan demikian, sistem E-TGA dapat mendukung transformasi digital layanan akademik dan menjadi solusi pengelolaan Tugas Akhir yang berkelanjutan di Politeknik Negeri Lhokseumawe.
Analisis Kesiapan Infrastruktur Teknologi Informasi Rumah Sakit dalam Mendukung Layanan Digital Berdasarkan COBIT 2019 dan ITIL 4 Faresya, Natasya; Fikry, Muhammad; Asrianda, Asrianda
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10408

Abstract

Abstrak - Transformasi digital di sektor kesehatan mendorong rumah sakit untuk mengimplementasikan berbagai layanan digital guna meningkatkan kualitas pelayanan dan efisiensi operasional. Keberhasilan implementasi layanan digital sangat bergantung pada kesiapan infrastruktur teknologi informasi yang mencakup aspek tata kelola dan manajemen layanan TI. Penelitian ini bertujuan untuk menganalisis kesiapan infrastruktur teknologi informasi dalam mendukung implementasi layanan digital rumah sakit menggunakan kerangka kerja COBIT 2019 dan ITIL 4. Metode penelitian yang digunakan adalah pendekatan kualitatif dengan desain studi kasus, melalui pengumpulan data berupa wawancara, observasi, dan studi dokumentasi. Analisis dilakukan dengan memetakan kondisi eksisting infrastruktur TI rumah sakit terhadap domain dan praktik yang relevan pada COBIT 2019 serta praktik manajemen layanan pada ITIL 4. Hasil penelitian menunjukkan bahwa secara umum infrastruktur teknologi informasi rumah sakit berada pada tingkat kesiapan yang cukup untuk mendukung layanan digital, namun masih terdapat beberapa aspek yang perlu ditingkatkan, khususnya pada pengelolaan layanan, pengendalian risiko, dan penyelarasan antara TI dan kebutuhan bisnis. Penelitian ini diharapkan dapat menjadi acuan bagi manajemen rumah sakit dalam merumuskan strategi peningkatan kesiapan infrastruktur TI secara berkelanjutan.Kata kunci: Kesiapan Infrastruktur TI; Layanan Digital Rumah Sakit; COBIT 2019; ITIL 4; Abstract - Digital transformation in the healthcare sector encourages hospitals to implement various digital services in order to improve service quality and operational efficiency. The success of digital service implementation is highly dependent on the readiness of information technology infrastructure, which includes aspects of IT governance and IT service management. This study aims to analyze the readiness of information technology infrastructure in supporting the implementation of digital hospital services using the COBIT 2019 and ITIL 4 frameworks. This research employs a qualitative approach with a case study design, where data are collected through interviews, observations, and document analysis. The analysis is conducted by mapping the existing conditions of the hospital’s IT infrastructure to the relevant domains of COBIT 2019 and the service management practices of ITIL 4. The results indicate that, in general, the hospital’s information technology infrastructure demonstrates an adequate level of readiness to support digital services. However, several aspects still require improvement, particularly in IT service management, risk control, and alignment between information technology and business needs. This study is expected to serve as a reference for hospital management in formulating strategies to enhance IT infrastructure readiness in a sustainable manner. Keywords: IT Infrastructure Readiness; Digital Hospital Services; COBIT 2019; ITIL 4;
SENTIMENT CLASSIFICATION OF PUBLIC PERCEPTIONS ON RP200 TRILLION HIMBARA STIMULUS USING NAÏVE BAYES Wan Sobri Amin; Muhammad Fikry; Rahmad Abdillah; Surya Agustian
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.500

Abstract

The government's policy in the form of a fund stimulus of Rp200 trillion to the Himpunan Bank Milik Negara (HIMBARA) is a strategic step to maintain national economic stability and encourage real sector recovery. However, the implementation of public policy is inseparable from the response and public perception that develops on social media. This study aims to classify public sentiment towards the Rp200 trillion fund stimulus policy to Bank HIMBARA based on Instagram user comments and test the performance of the Naïve Bayes Classifier method in analyzing public policy sentiment. This study uses a quantitative approach with text mining and machine learning methods. Data in the form of 1.309 Instagram comments was collected through web scraping techniques from several online media accounts, then processed through text preprocessing and manual labeling stages into positive, neutral, and negative sentiments. Feature weighting was carried out using TF-IDF, then the data were classified using Multinomial Naïve Bayes and Complement Naïve Bayes. The results show that the Complement Naïve Bayes model achieved the best performance with an accuracy of 84%, an F1-score of 81%, and a high ROC-AUC value. These findings indicate that the majority of public sentiment toward the stimulus policy tends to be positive, and that the Naïve Bayes method is effective for social media–based sentiment analysis.
Analysis of Apache Hadoop Architecture in Supporting Large-Scale Data Processing Muhammad Dhuha, Teuku Nabil; Asrianda; Muhammad Fikry
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.711

Abstract

The rapid development of information technology has led to the exponential growth of data generated from various sectors, such as healthcare services, social media, information systems, and other digital activities. This condition has given rise to the concept of big data, which cannot be optimally processed using conventional data processing technologies. Therefore, distributed computing platforms are required to efficiently handle large-scale data storage and processing. Apache Hadoop is one of the widely used big data technologies due to its distributed architecture that supports scalability, parallel processing, and fault tolerance. This study aims to analyze the architecture of Apache Hadoop and explain the role of each of its components in supporting large-scale data processing. The research method employed is a qualitative literature study, conducted through the review of books, scientific articles, and related publications on Hadoop. The results indicate that Hadoop consists of three main components: the Hadoop Distributed File System as a distributed storage system, MapReduce as a programming model for parallel data processing, and Yet Another Resource Negotiator, which functions in cluster resource management and scheduling. The integration of these components enables Hadoop to manage large-scale data in a reliable and distributed manner. However, Hadoop has limitations related to its batch-based processing model, which is less suitable for real-time processing needs, thus requiring consideration of complementary technologies according to application requirements.
Analisis Performa Voice Recognition Pada Smart Speaker Menggunakan Metode Random Forest Yani, Muhammad; Fikry, Muhammad; Hasibuan, Arnawan; Nurdin; Munirul Ula; Husaini
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID654

Abstract

The development of Internet of Things (IoT) and artificial intelligence technology has driven the increasing use of voice user interfaces (VUI) as a more natural form of human-computer interaction. One widely used VUI implementation is voice recognition-based smart speakers. Despite its widespread adoption, voice recognition performance on smart speakers is not necessarily optimal when used in real-world conditions, particularly in far-field scenarios that are influenced by user distance, environmental noise, and system response time. This study aims to analyze and compare the voice recognition performance of Amazon Alexa smart speakers and the Interactive Speaker System as a non-vendor comparison system. Testing was conducted at varying user distances in a non-soundproof room to represent real-world operational conditions.The obtained performance data was analyzed using the Random Forest method as a classification tool due to its ability to handle multivariate data and nonlinear relationships between variables. The results showed that variations in user distance significantly affected the voice recognition performance of both systems, with a tendency for performance to decrease as distance increased. In addition, differences in system architecture characteristics also influenced the level of resilience to environmental conditions. The application of the Random Forest method also enabled the identification of dominant factors that influence the success of voice recognition. This research is expected to provide theoretical contributions in the study of voice recognition performance in far-field scenarios, as well as practical contributions as a basis for consideration in the selection and development of more reliable voice-based interaction systems in real environments.
Co-Authors -, Yusra Abdillah, Rahmad Ahadi, Ridho Alwis Nazir Ananda, Nuari Andini, Nanda Anggraeni . Anggraeni, Ni Ketut Pertiwi Anna Marina Arnawan Hasibuan Asrianda Asrianda Ayu Fransiska Baehaqi Bahari, Bayu Dwi Prasetya Damayanti, Elok Dermawan, Jozu Detha Yurisna Dinata, Ferdian Arya Diqti, Fadillah Fauziah Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Eko Sumartono, Eko Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Elvina Afriani Fadhilah Syafria Fakhrezi, Muhammad Dzaki Faresya, Natasya Febi Yanto Fitri Insani Fitri Insani Harahap, Nazaruddin Safaat Hasugian, Leonardo Hidayat, Rizki Husaini Ibnu Surya Iis Afrianty Inggih Permana Khaidar, Al kurnia, fitra Lestari Handayani Lola Oktavia Lola Oktavia Mei Lestari, Mei Muhammad Abdillah Muhammad Affandes Muhammad Dhuha, Teuku Nabil Muhammad Irsyad Muhammad Ravil Muhammad Yani, Muhammad Munadila, Aura Munirul Ula Naharuddin Naharuddin Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Ndruru, Arlan Joliansa Nurcholis Sunuyeko, Nurcholis Nurdin Nurdin Nurdin Nurhapiza, Nurhapiza Oktavia, Lola Pizaini Pizaini Prananda, Alga Putra, Wahyu Eka Putri Mardatillah Rahma Yunita, Rahma Rahmat Rizki Hidayat Rahmatillah, Siska Yuna Ramadanu Putra Reski Mai Candra Rinaldi Syarfianto Ritonga, Sinta Wahyuni Sagala, Ruflica Saputra, Ikhsan Dwi Sayed Omas Tutus Arifta Sayed Sentot Imam Wahjono Sofiah Surya Agustian Suwanto Sanjaya Taufik Hidayat Tiara Dwi Arista Wan Sobri Amin Wirdiani, Putri Syakira Yani, Muhamamd Yani, Susmi Syahfrida Yaskur Bearly Fernandes Yenggi Putra Dinata Yolanda, Khovifah Yossie Yumiati Yuda Zafitra Fadhlan Yusra Yusra Yusra . YUSRA YUSRA Yusra, Yusra Zukhruf, Muhammad Firmansyah