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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) 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 Smatika Jurnal : STIKI Informatika Jurnal
Firman Noor Hasan
Universitas Muhammadiyah Prof. Dr. Hamka
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FAKTOR-FAKTOR YANG MEMPENGARUHI MINAT CALON MAHASISWA BARU MENDAFTAR PADA FTII UHAMKA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) Rahman Malik, Luqman Abdur; Kamayani, Mia; Hasan, Firman Noor
Infotech: Journal of Technology Information Vol 9, No 1 (2023): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v9i1.163

Abstract

In accepting new students at Prof. University. Dr. Hamka, many prospective students or parents of students are looking for registration information, this is a great opportunity for Uhamka to gain the sympathy of prospective students to register at Uhamka, especially the Faculty of Industrial and Informatics Technology. The problem in this study is that there is no data processing related to the factors that influence the interest of prospective new students to choose the Faculty of Industrial and Informatics Technology (FTII) Uhamka. The purpose of this study was to determine the factors that influence the interest of prospective new students in choosing majors at the Faculty of Industrial and Information Technology (FTII) Uhamka. The attributes used in this study were 10 attributes, namely full name, major, tuition fee, FII location with domicile, presence of friends/family, accreditation, facilities, PMB services, PMB information, and information of interest. The method that researchers use in this study is the K-Nearest Neighbor Algorithm (K-NN). From the results of testing the researchers used the K-5 fold technique and the confusion matrix obtained an average accuracy of 72.5%, which means it is good.
SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN PRODUK IDEAL PADA REMANUFACTURE TONER MENGGUNAKAN METODE FUZZY TSUKAMOTO Erizal, Erizal; Hasan, Firman Noor
Infotech: Journal of Technology Information Vol 10, No 1 (2024): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i1.246

Abstract

In the business world, every business owner can of course experience losses when running their business. These losses can be caused by various kinds of obstacles, one of which is the accumulation of products that are of little interest so that only a few are sold. Therefore, we need a system that can determine the ideal sales product so that it can minimize losses and product buildup and help buyers recommend products to buy. This research uses Tsukamoto's fuzzy approach and the use of MatLab as a computerized computing tool, which allows careful comparison between manual calculations and tools. The results obtained provide a recommendation that the CZ192A Toner Remanufacture product is an Ideal product, the CE255A Toner Remanufacture product is a Non-Ideal product, and the Q7516A Toner Remanufacture product is an Ideal product.ABSTRAKDalam menjalani dunia perbisnisan, setiap pemilik badan usaha tentunya dapat mengalami kerugian ketika menjalankan bisnisnya. Kerugian tersebut dapat disebabkan oleh berbagai macam kendala salah satunya adalah penumpukan produk yang sedikit peminatnya sehingga hanya beberapa yang laku terjual. Oleh karena itu dibutuhkan sebuah sistem yang dapat menentukan produk penjualan yang ideal sehingga dapat meminimalisir kerugian serta penumpukan produk serta membantu pembeli dalam merekomendasikan produk yang akan dibeli. Penelitian ini menggunakan pendekatan fuzzy tsukamoto dan penggunaan MatLab sebagai alat komputasi terkomputerisasi, yang memungkinkan perbandingan yang cermat antara perhitungan manual dan tools. Hasil yang diperoleh memberikan rekomendasi bahwa produk Remanufacture Toner CZ192A merupakan produk Ideal, produk Remanufacture Toner CE255A merupakan produk Tidak Ideal, dan produk Remanufacture Toner Q7516A merupakan produk Ideal.
Penerapan Business Intelligence & Online Analytical Processing untuk Data-Data Penelitian dan Luarannya pada Perguruan Tinggi Menggunakan Pentaho Hasan, Firman Noor; Sudaryana, I Ketut
Infotech: Journal of Technology Information Vol 8, No 2 (2022): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v8i2.143

Abstract

Utilization of ICT in various sectors is needed, especially those related to business impact and strategy. Information technology is the backbone of the sustainability of businesses, companies, and organizations. Companies that are able to utilize ICT well, therefore indirectly adapt to the times and strive to excel from competitors. The method used refers to the steps recommended by Carlo Vercellis. The software and tools used are open source based, such as Pentaho Data Integration for processing extract, transform, load (ETL), Pentaho Community Edition for dashboards, Pentaho Report Designer for report generation, and Mondrian OLAP for displaying multidimensional data. The results of this study conclude application of business intelligence in universities is very easy and efficient, the use of a dashboard that is presented visually and interactively is very helpful for leaders in viewing existing research data. So it is very helpful for organizations, especially university leaders in making decisions.
Analisis Sentimen Terhadap Penggunaan Chatgpt Berdasarka Twitter Menggunakan Algoritma Naïve Bayes Transiska, Dwi; Febriawan, Dimas; 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.v8i2.7540

Abstract

Chatbots can assist by fostering collaboration between users and companies or organizations. In today's world, artificial intelligence through chatbots has become one of the mainstays in handling various problems. One of the well-known types of chatbots is ChatGPT, which utilizes NLP (Natural Language Processing) technology to understand and respond to user queries and requests. ChatGPT, as a device that is very easy to use for all circles, has a fairly simple interface but does not cause boredom, and the speed in responding to commands given, this is an added value of ChatGPT. Despite the myriad of conveniences offered, ChatGPT also raises concerns on the negative side. The negative side is that there are many concerns that arise, starting from the rampant spread of hoaxes and misunderstandings on social media. The advantages and disadvantages that have been explained above, researchers are encouraged to find out the truth from the public's response regarding ChatGPT more deeply so that this sentiment analysis research is made. Moreover, research related to sentiment analysis can be said to be quite an answer to the confusion of public responses outside related to ChatGPT. This research also starts from the process of Data retrieval on Twitter social media using Rapidminer, in this process the researcher uses the Twitter API token on the Rapidminer application so that it can be obtained. The data that has been obtained is then cleaned through the preprocessing process using the features available in Rapidminer, the result of this process is that the data becomes clean. After being cleaned through preprocessing, it is then labeled as positive or negative which will later be classified by the Naïve Bayes algorithm. This classification aims to divide between positive sentiment and negative sentiment. After performing classification, the data is then evaluated using a confusion matrix and the results are obtained with an accuracy value of 96.55%, a precision value of 89.19%, and a recall value of 95.18%.
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%).
Analisis Sentimen Warganet Terhadap Keberadaan Juru Parkir Liar Menggunakan Metode Naive Bayes Classifier Mukti, Avis Tantra; Hasan, Firman Noor
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6982

Abstract

The still high rate of poverty in Indonesia causes many impacts, one of which is the emergence of illegal parking attendants. This condition continues to be supported by the creation of a parking area for business actors for their visitors. The parking areas provided are often free, and even have signs saying so. However, there are individuals who use the free parking space to earn income. There are many netizens' sentiments regarding the phenomenon of lying parking attendants on social media. Therefore, in this research, an analysis was used in the form of netizen sentiment towards illegal parking attendants on social media X using Naïve Bayes. The main objective of this research is to understand the public's feelings towards the existence of illegal parking attendants operating in the parking area. The dataset used in this analysis was 905 taken from social media The results of this research netizens felt very annoyed, angry and disturbed by the presence of illegal parking attendants operating. This is proven by the results of negative sentiment which dominates 93% of the total data or as many as 841 negative sentiments regarding this phenomenon.
Analisis Sentimen Kegiatan Pembersihan Sampah Pada Media Sosial X Menggunakan SVM dan Naïve Bayes Nugroho, Dendy Aprilianto; 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.v8i2.7562

Abstract

Human daily activities inevitably produce waste, which negatively impacts environmental balance due to the bad habit of indiscriminately disposing of waste. As a result of this issue, there is a youth community named Pandawara Group that wants to help clean up trash on Sukabumi Beach. However, their initiative faced rejection from the local village chief and youth organization, sparking various opinions on social media platform X. Consequently, this research seeks to analyze public sentiment towards Pandawara Group's waste cleanup efforts at Sukabumi Beach using Support Vector Machine and Naïve Bayes methods. The objective is to gauge positive and negative sentiments and compare the accuracy of Support Vector Machine and Naïve Bayes.  In this sentiment analysis using 2,339 datasets, the highest accuracy was achieved using the Support Vector Machine method at 91.67%, whereas the Naïve Bayes method only achieved 63.89%. Thus, it can be concluded that Support Vector Machine is superior in classifying sentiments regarding Pandawara Group's waste cleanup activities at Sukabumi Beach compared to Naïve Bayes.
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 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%.
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 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 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 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 Bagus Andreyanto 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 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 Widyastuti Andriyani Windi Al Azmi Wulandari, Sania Zahra, Khofifah Humaeroh Az Zaini, Fachri Zuhri Halim Zuhri Halim, Zuhri