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All Journal TEKNIK INFORMATIKA Sainteks Jurnal Pseudocode SMATIKA Jurnal Ilmiah KOMPUTASI Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA InComTech: Jurnal Telekomunikasi dan Komputer Jurnal SOLMA Prosiding Seminar Nasional Teknoka Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sisfokom (Sistem Informasi dan Komputer) IJID (International Journal on Informatics for Development) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science FIKROH: JURNAL PEMIKIRAN DAN PENDIDIKAN ISLAM Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer SmartComp Fikroh: Jurnal Pemikiran dan Pendidikan Islam JURNAL TEKNIK INFORMATIKA DAN KOMPUTER Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) SAMBARA: Jurnal Pengabdian Kepada Masyarakat Cosmic Jurnal Teknik BHAKTI JIVANA Smatika Jurnal : STIKI Informatika Jurnal
Firman Noor Hasan
Universitas Muhammadiyah Prof. Dr. Hamka
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Analisis Sentimen Opini Masyarakat Terkait Pelayanan Jasa Ekspedisi Anteraja Dengan Metode Naive Bayes Affandi, Irfan Ricky; Hasan, Firman Noor; Pratiwi, Nunik; Halim, Zuhri
Jurnal Linguistik Komputasional Vol 5 No 2 (2022): Vol. 5, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v5i2.107

Abstract

Peningkatan jumlah transaksi pada berbagai platform e-commerce mempunyai implikasi terhadap peningkatan penggunaan jasa ekspedisi. Salah satu perusahaan jasa ekspedisi yang ada di Indonesia yaitu anteraja, dimana perusahaan tersebut harus bisa memiliki inovasi untuk mempunyai hubungan serta memberikan pelayanan yang terbaik bagi penggunanya. Saat ini banyak pengguna layanan anteraja mempunyai pendapat yang beragam terhadap layanan mereka pada media sosial twitter. Penelitian ini menerapkan teknik sentiment analysis untuk membantu mengevaluasi, menganalisis, menilai, serta mengetahui sikap masyarakat terhadap pelayanan Anteraja. Metode untuk mengkategorikan sentimen yang digunakan oleh peneliti yaitu menerapkan algoritma naive bayes yang mempunyai akurasi tinggi, serta prosesnya sederhana dan cepat. Peneliti juga menggunakan bantuan perangkat lunak python untuk proses pengambilan dataset pada twitter serta rapidminner studio untuk pengolahan data serta penerapan algoritma. Hasil dari proses pengolahan data yang dilakukan oleh peneliti didapatkan dari 1180 data, jumlah kategori yang paling banyak yaitu kategori sentimen positif sebesar 638 lalu kategori sentimen negatif sebesar 493 sedangkan paling sedikit yaitu kategori sentimen netral sebanyak 49. Hal ini menunjukkan dari 1180 data bahwa banyak masyarakat yang menyukai pelayanan yang diberikan oleh jasa ekspedisi Anteraja, namun tidak sedikit masyarakat yang masih kurang puas terhadap pelayan yang diberikan. Nilai akurasi penerapan algoritma naive bayes dalam penelitian ini diperoleh persentase sebesar 85.06% yang menunjukkan bahwa data tersebut dapat digunakan sebagai dasar bagi perusahaan untuk pertimbangan pengambilan keputusan.
Perancangan Sistem Pengelolaan Data Persediaan Barang Menggunakan Visual Basic Pada PT.Unibless Indo Multi Hazbi Santoso; Fachri Zaini; Firman Noor Hasan
Prosiding Seminar Nasional Teknoka Vol 8 (2023): Proceeding of TEKNOKA National Seminar - 8
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

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

Abstract

PT Unibless Indo Multi is engaged in IT Services, IT Trading, and IT Outsourcing. In this paper focuses on IT Trading, which includes the sale and rental of IT hardware, the provision of office equipment, the provision of shelves and archival support equipment and others. The author finds problems that occur specifically in the inventory of goods in processing inventory data still using simple applications, which causes the company's performance to be less than optimal. The author aims to solve existing problems by designing and building an inventory data management system using visual basic with the waterfall method, Unified Modeling Language (UML), Balsamiq Mockup and using a Microsoft Access database. Based on the test results using blackbox testing, the system runs as expected. After testing, 92.5 out of 7 respondents agreed that the system is easy to understand and use.
Analisis Sentimen Terhadap Pelayanan TransJakarta Berdasarkan Tweets Menggunakan Metode Naïve Bayes Classifier Muflih, Hilmy Zhafran; Hasan, Firman Noor
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1927

Abstract

The high use of private transportation in Indonesia, especially in the Jakarta area, causes several impacts, one of which is traffic jams. This congestion condition can be reduced by public transportation. It is hoped that public transportation can now reduce the level of congestion in Jakarta. One of the public transportation in Jakarta is TransJakarta. TransJakarta is a form of transportation that can carry a relatively large number of passengers and TransJakarta offers various facilities to users, such as the availability of priority seating, stops that are quite comfortable, comfortable conditions on the bus plus low prices so that it gets various responses from users who led researchers to conduct research on the views of TransJakarta users regarding TransJakarta services, whether TransJakarta users' responses were positive or negative. The purpose of this research is to understand whether users are satisfied or not with the services provided by TransJakarta. The method used in the research is the Naïve Bayes Classifier algorithm which is used to carry out the sentiment analysis process regarding TransJakarta services with the help of the RapidMiner application. The data collected by researchers was 773 tweet data obtained via social media X to be used as a dataset. The results of sentiment analysis from the Naïve Bayes Classifier algorithm obtained 80.6% or 623 negative sentiments and 19.4% or 150 positive sentiments from 773 datasets. The results of the confusion matrix evaluation obtained an accuracy value of 73.96%.
Evaluating Wind Deflector Effect on Cargo Vans Aerodynamic Drag Using Computational Fluid Dynamics Agus Fikri; Ariyansah, Riyan; Firman Noor Hasan; Oktarina Heriyani; Rosalina; Sistani, Muhammad Ghiffar
Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa & Inovasi Volume 6 Nomor 2 Tahun 2024
Publisher : Fakultas Teknik Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/asiimetrik.v6i2.6073

Abstract

Suboptimal design and body shape in freight transport vehicles can lead to increased aerodynamic drag. To address this issue, the use of wind deflectors is proposed as a solution to reduce aerodynamic resistance in cargo vans. The methodology employed in this research involves Computational Fluid Dynamics (CFD) simulations using the Ansys Fluent R2 2023 software. CFD simulations were conducted on the design of a cargo box vehicle with variations in Wind Deflector Models 1, 2, and 3, employing identical boundary condition parameters. The results of the CFD simulation for Wind Deflector Model 3 exhibited the lowest drag force at 1.1531116 Newton and a drag coefficient of 0.37031338. In conclusion, a comprehensive analysis of the CFD simulation results provides valuable insights into the intricate aerodynamic implications of Wind Deflector variations on cargo vans. Therefore, it is concluded that Wind Deflector Model 3 emerges as the optimal choice, showcasing superior aerodynamic characteristics.
Analisis Sentimen Masyarakat Terhadap Rencana Kenaikan PPN 12% Di Indonesia Pada Media Sosial X Menggunakan Metode Decision Tree Hardyatman, Intan Diah; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6573

Abstract

This study analyzes public sentiment towards the planned increase of Value Added Tax (VAT) to 12% in Indonesia using data from X social media. The VAT hike could trigger an increase in overseas spending and higher prices for products and services in Indonesia, potentially reducing sales and weakening industries. This proposal also received widespread attention on social media X. The VAT increase plan has pros and cons, triggering many discussions on social media. The Decision Tree classification method was used to process the data obtained through crawling and text preprocessing. This research compares 80% training data and 20% test data consisting of 1000 data, with details of 285 negative sentiments and 715 positive sentiments in the dataset. In this case, it can be described that X social media users towards the plan to increase VAT by 12% in Indonesia tend to be positive. This research aims to analyze people's sentiment towards the plan to increase VAT by 12% in Indonesia using Decision Tree and identify factors that influence the sentiment. The results of the analysis show that Decision Tree succeeded in increasing the accuracy by 81.34% of sentiment classification compared to previous methods, such as Naïve Bayes with an accuracy rate of 63.1%. The results of this study are expected to help the government in a more responsive fiscal policy.
Perbandingan Tingkat Akurasi Algoritma Naïve Bayes dan Support Vector Machine Dalam Analisis Sentimen Pengguna Aplikasi ShopeePay Pada Google Play Store Hilmi Ammar; Fadli Al Gani; Muhammad Rifansyah; Firman Noor Hasan
Prosiding Seminar Nasional Teknoka Vol 9 (2024): Proceeding of TEKNOKA National Seminar - 9
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/teknoka.v9i1.17549

Abstract

This research aims to analyze user sentiment towards the ShopeePay application using the Naïve Bayes and SVM algorithms with data obtained through web scraping. Of the 1500 data obtained through scraping, 63 empty data were removed in the cleaning process, leaving 1437 data. This data was then divided into a training set (1149 data) and a test set (288 data). The results showed that the Naïve Bayes algorithm achieved an accuracy of 84.38%, a precision of 79.73%, a recall of 88.72%, and an F1-score of 83.99%, while the Support Vector Machine (SVM) algorithm achieved an accuracy of 80.56%, a precision of 84.07%, a recall of 71.43%, and an F1-score of 77.24%. Overall, Naïve Bayes performed better than Support Vector Machine, especially Naïve Bayes was superior in detecting positive sentiment, while SVM was better in detecting negative sentiment. Data visualization shows that out of 1437 users, around 52.7% gave positive reviews and 47.3% negative reviews, with a diverse rating distribution from users. Based on this distribution, the ShopeePay application user experience can be categorized as predominantly positive, with a difference of 5.4% indicating the difference between 52.7% positive reviews and 47.3% negative reviews from ShopeePay application users.
ANALYSIS OF PUBLIC SENTIMENT RELATED TO THE FAILURE OF INDONESIA TO HOST U-20 USING MULTINOMIAL NAÏVE BAYES CLASSIFIER Zaini, Fachri; Sari, Jessica Windi; Hasan, Firman Noor
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1209

Abstract

The case of Indonesia's failure to host the U-20 World Cup in 2023 has become a hot topic of discussion in Indonesia. The rejection of the Israel U-20 national team and security factors by FIFA are considered the main reasons for the cancellation. This raises many issues and controversies from various parties. In this study, sentiment analysis using the Naive Bayes algorithm was conducted. Researchers use the naive bayes algorithm because this algorithm has high accuracy with simple calculations. The data obtained in this study came from 250 tweets of Twitter data with a ratio of training and test data of 7:3. The results showed good data classification with 97.26% accuracy, 93.33% precision, and 100% recall. In conclusion, the classification model developed can describe public sentiment related to Indonesia's failure in the U-20 World Cup well.
Business Intelligence Visualisasi Data Penerimaan Mahasiswa Baru Menggunakan Tableau di Universitas ABC Anhari, Tirta; Alim, Endy Sjaiful; Rizkiawan, M. Asep; Hasan, Firman Noor; Aulia, Muhammad Fathan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.570

Abstract

This study aims to analyze the application of Business Intelligence (BI) using Tableau in the new student admission process at ABC University. Tableau is used to visualize admission data for the period 2021 to 2023, including the number of applicants, geographic distribution, and course preferences. The research methodology involves data collection, cleaning, and integration which is then visualized in an interactive dashboard. The results showed a decrease in the number of applicants during the study period, with the lowest applicants in 2024. Geographic distribution analysis shows that DKI Jakarta and West Java provinces still dominate, indicating the need for expansion in conducting promotions and also data-based marketing strategies. In addition, the shift in the interest of applicants from Communication Science study programs to Pharmacist and Management Professions is an important finding, indicating a changing trend in prospective students' preferences for the fields of Communication Science and Business. This study concludes that the implementation of BI using Tableau provides significant benefits in improving the efficiency of decision-making, expanding the range of admissions, and strengthening the competitiveness of ABC University amid changing educational trends. The findings contribute to the literature related to BI implementation in the education sector and recommend further development to optimize university management in the future
Implementation of User Sentiment with Naïve Bayes Algorithm to Analyze LinkedIn Application Regarding Job Vacancies in the Play Store Al Ghozi, Dhiyauddin; Hasan, Firman Noor
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7879

Abstract

Mobile applications have become an important part, one of which is the LinkedIn application which is a mobile application that focuses on the recruitment process, job search and as a professional networking platform which is now increasingly relevant, especially in Indonesia. The methodology involves data collection, data preprocessing, data labeling, and application of the Naïve Bayes algorithm. Sentiment analysis can be used as a reference to improve the quality of an application and the level of user satisfaction as well as knowing the number of positive and negative sentiments in user feedback. The 999 data obtained were then divided into 60% training data and 40% test data. In this analysis, negative sentiment outweighs positive sentiment, with a total of 539 negative reviews and 460 positive reviews. Based on evaluation using the confusion matrix, accuracy results were 95.74%, precision was 100%, and recall was 91.46%. This research aims to provide insight into the communication and interaction patterns of LinkedIn users in relation to job opportunities and overall sentiment towards the platform.
Analisis Sentimen Calon Presiden 2024 di Media Sosial X Menggunakan Naive Bayes dan SMOTE Sunata, Muhamad Hafidz Ardian; Irwiensyah, Faldy; Hasan, Firman Noor
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7708

Abstract

In the era of digital advancement, the utilization of social media has surged, enabling individuals to express their viewpoints openly. This research underscores the utilization of social media platform X as the primary avenue for users to express their opinions, particularly on political matters, notably within the framework of the presidential election. Sentiment analysis techniques, specifically employing the Naïve Bayes algorithm and the Synthetic Minority Oversampling (SMOTE) method, have been the central focus of inquiry to infer people's inclinations toward presidential candidates. Despite numerous antecedent studies, deficiencies persist in terms of precision and data imbalance. This study endeavors to enhance the efficacy of sentiment analysis by integrating the Naïve Bayes approach with SMOTE. By scrutinizing tweets on social media X spanning from December 12, 2023, to March 31, 2024, the data is categorized into positive and negative sentiments. The findings reveal that employing SMOTE bolstered accuracy to 88% for the Ganjar-Mahfud dataset, whereas accuracy without SMOTE languished at approximately 69% for the Anies-Imin dataset. Out of 1589 tweets conveying positive sentiments, approximately 27.7% were directed towards Anies-Imin, 28.7% towards Prabowo-Gibran, and 43.5% towards Ganjar-Mahfud. The preponderance of negative sentiments was aimed at Anies-Imin (41.5%) and Prabowo-Gibran (40.8%).
Co-Authors Abdillah, Allif Rizki Abdul Syakir Achmad Ramadhan Achmad Sufyan Aziz Afandi, Irfan Ricky Affandi, Irfan Ricky Afikah, Prista Afnan Sabili, Dian Ainurrafik Agus Fikri Agus Fikri Ahmad Rizal Dzikrillah Ahmad Rizal Dzikrillah Ahmad Roshid Ahmad Syahril Ahmad Syahril Al Ghozi, Dhiyauddin Alfandi Safira Alim, Endy Sjaiful Allif Rizki Abdillah Allif Rizki Abdillah Allif Rizki Abdillah Ammar Rusydi Ananda Prasta Warasati Janah Ananda, Ridha Faiz Andika Saputra Andriani, Vivi Andriyani, Widyastuti Anhari, Tirta Anwar Hidayat Ari Wibowo Arief Wibowo Arien Bianingrum Rossianiz Arvin Rafialdo Aulia, Muhammad Fathan Avorizano, Arry Azhar Haikal Anwar Azhar Haikal Anwar Azis Styo Nugroho Bagas Kembar Rezkyllah Bahrul Rozak Bahrul Rozak Bisma Indrawan Dan Mugisidi Dandie Triyanto Desty Afni Dewi Mayangsari Dian Ainurrafik Afnan Sabili Dian Ainurrafik Afnan Sabili Diana Fitri Lessy Diana Fitri Lessy Dimas Febriawan Dimas Febriawan Dimas Febriawan Dion Parisda Ray Djeli Moh Yusuf Doni Gunawan Rambe E Erizal Erizal Erizal Estu Sinduningrum Estu Sinduningrum Fachri Zaini Fadli Al Gani Fadli Hardiyanto Putra Fadli, Khairul Faisal Parsakh Nursyamsi Faisal Parsakh Nursyamsyi Fajar Sidik Faldy Irwiensyah Faldy Irwiensyah Faldy Irwiensyah, Faldy Farhan Bias Purnama Putra Farhan Nufairi Farhan Nufairi Fathurrohman, Sewin Fauzan Setya Ananto Fauzi Kurniawan Fayakun Kun Febriandirza, Arafat Febriawan, Dimas Gusnul Mahesa Hafizh Dhery Al Assyam Handika, Yusuf Hanif, Isa Faqihuddin Hardyatman, Intan Diah Hazbi Santoso Hibatullah Faisal Hibatullah Faisal Hibatullah Faisal Hilmi Ammar Hilmy Zhafran Muflih I Ketut Sudaryana, I Ketut Ibnu Suhada Indra Ramadhan Indra Ramadhan Indriyanti, Prastika Intania Widyaningrum Irawati Irawati Irfan Ricky Afandi Irfan Ricky Affandi Irma Wahyuningtyas Isnan Wisnu Prastiyo Kamayani, Mia kivandi Nugroho Krisna, Mohammad Dito Dwi Kurniyati Nur Lathifah Dini Rachmawati Lingga Lingga Lingga Lita Astri Pramesti Luqman Abdur Rahman Malik Lutfi Triyuli Evana Rizki Luthfi Akbar Ramadhan M. Asep Rizkiawan Meliyawati MILASARI, LISA ASTRIA Mohammad Akhdaan Juliandra Muchammad Sholeh Muchammad Sholeh Muflih, Hilmy Zhafran Muhamad Saiful Arif Muhammad Abid Fajar Muhammad Ardhi Ryan Saputra Muhammad Ghiffar Sistani Muhammad Ikhwan Muhammad Ikhwan Muhammad Rafly Al Fattah Zain Muhammad Ridwan Muhammad Rifansyah Mukti, Avis Tantra Mutiara Zahra Arifin Nanang Juhandi Hermawan Neneng Siti Maryam Nisa Qonita Rizkina Nofendri, Yos Nugroho, Dendy Aprilianto Nunik Pratiwi Oktarina Heriyani Pamungkas, Dimas Panji Islami Anakku Pavita, Rachma Pranata, Ananda Bagas Prisilia Talakua Prista Afikah Purnamaningsih, Ine Rahayu Putri, Kirana Alyssa Rafli Erlangga Rahman Malik, Luqman Abdur Rahmatullah, Ahmad Faiz Ramadhita, Nindia Fitri Ramzah, Harry Reisa Inayah Rian gustini Ridwan Bagus Andreyanto Ridwan Maulana Subekti Rika Nurhayati Riyan Ariyansah Rizki Alamsyah Rizki Kamelia Rizky Ramdhani Rosalina Rosalina Rozak, Bahrul Saputra, Ramadani Sari, Jessica Windi Sari, Laila Atikah Setiawan, Ahmat Sewin Fathurrohman Simamora, Silvia Damayanti Sinduningrum, Estu Sistani, Muhammad Ghiffar Siti Nurhaliza Sri Fitriani Sunata, Muhamad Hafidz Ardian Syahri, Alfi Tasya Rizki Salsabilla Tia Anggita Sari Transiska, Dwi Wahyu Stiyawan Wahyuningtyas, Irma Wanda Aulia Windi Al Azmi Wulandari, Sania Zahra, Khofifah Humaeroh Az Zaini, Fachri Zuhri Halim Zuhri Halim, Zuhri