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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) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa 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) Teknosains : Jurnal Sains,Teknologi dan Informatika 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) Cosmic Jurnal Teknik BHAKTI JIVANA
Firman Noor Hasan
Universitas Muhammadiyah Prof. Dr. Hamka
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Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes Wulandari, Sania; Hasan, Firman Noor
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7520

Abstract

Thrifting is an increasingly popular second-hand shopping activity in Indonesia, especially among millennials and generation Z as a cost-saving shopping alternative. Thrifting activities have a clear positive impact on the Indonesian people in protecting the environment by reducing the purchase of new goods. However, thrifting is considered illegal and can harm the domestic textile industry. So sentiment analysis needs to be done to find out how people respond to thrifting activities. This study aims to calculate the number of positive and negative comments from Twitter users, and find out how accurately the Naïve Bayes algorithm is used in the classification. The data used is taken from Twitter social media as many as 900 tweets, then processed through several advanced stages such as pre-processing which consists of cleansing, tokenize, and filter stopwords. Then at the labeling stage the data is divided into training data and test data with a ratio of 60:40. After being classified using the Naïve Bayes algorithm, the results obtained tend to be positive with a total of 368 positive comments and 181 negative sentiments. After going through the evaluation stage, the accuracy value is 95.92%, the precision value is 95.76%, and the recall value is 97.41%. The evaluation results show that the Naïve Bayes algorithm is proven to have a high level of accuracy used in classification.
Analysis Sentiment of Community Response on Cooking Oil Price Increase Policy With Naive Bayes Classifier Algorithm Hasan, Firman Noor; Sidik, Fajar; Afikah, Prista
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.v5i2.99

Abstract

Cooking oil is a basic need for Indonesian people. Indonesia experienced a shortage of oil in March 2022. This has become a hot conversation on Twitter social media last March, many people think positively or negatively. But behind it all there are different assessments of the parties who feel the pros and cons, various parties have different points of view. In this article, we conduct a sentiment analysis on the public's response to the scarcity of cooking oil using a dataset obtained from the Twitter digital platform. This article aims to classify tweets related to the scarcity of cooking oil into positive and negative sentiments using a machine learning strategy using the Naive Bayes method. This algorithm was chosen to make it easier for the public to make choices and to know the level of accuracy of the method, where the level of accuracy obtained from the nave Bayes classifier method 72%.
Analisis Sentimen Opini Masyarakat Terkait Penyelenggaraan Sistem Elektronik Menggunakan Metode Logistic Regression Hanif, Isa Faqihuddin; Affandi, Irfan Ricky; Hasan, Firman Noor; Sinduningrum, Estu; 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.v5i2.103

Abstract

Keamanan data menerapkan salah satu hal terpenting dalam menggunakan internet. Kebocoran data pribadi pengguna di internet dapat dimanfaatkan oleh orang yang tidak berkepentingan untuk berbagai macam tindakan kriminal. Upaya melindungi data pribadi masyarakat Indonesia, pemerintah melalui Kemkominfo membuat serta menerapkan sebuah kebijakan Penyelenggaraan Sistem Elektronik (PSE). Dalam penerapan PSE mendapatkan berbagai macam opini dari masyarakat Indonesia salah satunya yaitu pada platform twitter. Opini yang dikeluarkan oleh masyarakat ada yang bersifat negatif, netral maupun bersifat positif. Adapun tujuan penelitian ini yaitu mengetahui jumlah sentimen negatif, netral serta positif terhadap opini masyarakat terhadap PSE dimana dataset tersebut diperoleh dari platform twitter serta mengetahui nilai akurasi dari hasil uji evaluasi dari penerapan algoritma Logistic Regression. Hasil luaran dari 1073 dataset tentang opini dari masyarakat terhadap PSE didapatkan sebanyak 126 sentimen bersifat negatif, sebanyak 657 sentimen bersifat netral serta sebanyak 291 sentimen yang bersifat positif dengan nilai akurasi penerapan algoritma Logistic Regression sebesar 79.07%. Hal tersebut memperlihatkan bagaimana opini masyarakat Indonesia yaitu sebagian besar setuju terhadap diberlakukannya PSE namun masih ada beberapa masyarakat yang belum bisa menerima kebijakan tersebut.
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): Januari 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.
EVALUASI USER EXPERIENCE PADA GAME GTA V ROLEPLAY SERVER INDOPRIDE MENGGUNAKAN METODE ENHANCED COGNITIVE WALKTHROUGH Zahra, Khofifah Humaeroh Az; Hasan, Firman Noor
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.v9i4.5659

Abstract

Grand Theft Auto (GTA) V merupakan video game action online berkonsep free roam dan story missions, yang diterbitkan oleh Rockstar Games. Dengan menggunakan FiveM, GTA V bisa dimodifikasi dan dimainkan secara roleplay dengan multiplayer dalam sebuah server. Di Indonesia, server Indopride Roleplay merupakan salah satu server GTA V Roleplay yang sudah berdiri selama 5 tahun. Server Indopride Roleplay jika memiliki user experience yang bagus, maka akan mudah menarik para player dan mampu bersaing di pasar server FiveM Indonesia. Penelitian ini bertujuan untuk melakukan evaluasi user experience dengan metode enhanced cognitive walkthrough, dalam mengevaluasi permasalahan dan rekomendasi peningkatan server Indopride Roleplay berdasarkan hasil evaluasi oleh para player. Dimana dengan menggunakan metode ini partisipan akan diberikan skenario task untuk menjawab pertanyaan yang berasal dari perspektif pengguna. Data partisipan yang diambil berdasarkan player yang sudah pernah bermain di server Indopride Roleplay. Setelah dilakukan evaluasi dengan metode enhanced cogntiive walkthrough, nilai masalah tertinggi terdapat pada matrix A dengan nilai 4,60 pada task importance 3 dan problem seriousness 4, lalu matrix B dengan nilai 3,10 pada problem type feedback dan problem seriousness 4, matrix C dengan nilai 1,75 pada problem type feedback dan task importance 1, kemudian matrix D dengan nilai 3,60 pada problem seriousness 4 dan task number 7, dan terakhir matrix E dengan nilai 1,25 pada problem type feedback dan task number 7.
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 Ammar Rusydi Ananda Prasta Warasati Janah Ananda, Ridha Faiz Andika Saputra Andriani, Vivi Anhari, Tirta Anwar Hidayat Ari Wibowo Arief Wibowo Arien Bianingrum Rossianiz Arvin Rafialdo Aulia, Muhammad Fathan Avorizano, Arry Azhar Haikal Anwar Azhar Haikal Anwar Bagas Kembar Rezkyllah Bahrul Rozak Bahrul Rozak 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 Erizal Erizal Estu Sinduningrum Estu Sinduningrum Fachri Zaini Fadli Al Gani Fadli Hardiyanto Putra Fadli, Khairul Faisal Parsakh Nursyamsi Faisal Parsakh Nursyamsyi Fajar Sidik 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 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 Affandi Isnan Wisnu Prastiyo Kamayani, Mia Krisna, Mohammad Dito Dwi Kurniyati Nur Lathifah Dini Rachmawati Lingga Lingga Lingga Lita Astri Pramesti Luqman Abdur Rahman Malik 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 Ikhwan Muhammad Ikhwan Muhammad Rafly Al Fattah Zain Muhammad Ridwan Muhammad Rifansyah Mukti, Avis Tantra Mutiara Zahra Arifin 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 Maulana Subekti Rika Nurhayati Riyan Ariyansah Rizki Alamsyah Rizki Kamelia Rizky Ramdhani Rosalina Rozak, Bahrul Saputra, Ramadani Sari, Jessica Windi Sari, Laila Atikah Setiawan, Ahmat 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 Widyastuti Andriyani Windi Al Azmi Wulandari, Sania Zahra, Khofifah Humaeroh Az Zaini, Fachri Zuhri Halim Zuhri Halim, Zuhri